Conditional Density Estimation via Least-Squares Density Ratio Estimation
Abstract
Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper, we propose a novel method of conditional density estimation that is suitable for multi-dimensional continuous variables. The basic idea of the proposed method is to express the conditional density in terms of the density ratio and the ratio is directly estimated without going through density estimation. Experiments using benchmark and robot transition datasets illustrate the usefulness of the proposed approach.
Cite
Text
Sugiyama et al. "Conditional Density Estimation via Least-Squares Density Ratio Estimation." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.Markdown
[Sugiyama et al. "Conditional Density Estimation via Least-Squares Density Ratio Estimation." Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010.](https://mlanthology.org/aistats/2010/sugiyama2010aistats-conditional/)BibTeX
@inproceedings{sugiyama2010aistats-conditional,
title = {{Conditional Density Estimation via Least-Squares Density Ratio Estimation}},
author = {Sugiyama, Masashi and Takeuchi, Ichiro and Suzuki, Taiji and Kanamori, Takafumi and Hachiya, Hirotaka and Okanohara, Daisuke},
booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics},
year = {2010},
pages = {781-788},
volume = {9},
url = {https://mlanthology.org/aistats/2010/sugiyama2010aistats-conditional/}
}